Sense My FEUP - April 2016 Data

SenseMyFEUP data

Data loading

Data is filtered by travelmode (car and bus) and date (April 2016).

Intersession times

Visualizing intersession times

All intersession time

Intersession time along the week

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Histogram intersession time.

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Number of sessions per way_id

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Filtering by sessions per way_id

ECDF

ECDF by #sessions

Lowest 35%

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

ggplotly(c1) 

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Removed 248 rows containing non-finite values (stat_bin).

Top 10% points

Map points April 2016

Showing maps

Traffic Map April Porto 2016 all

Traffic Map April 2016 all >50

Traffic day Map April 2016

Traffic night Map April 2016

Top 12 edges

Map Intersession time all 1.30h.

Map Intersession time (<1.3h) day.

Map Intersession time (<2h)night.

By date >50

Speeds >50

# Speed by way_id and session.\
avg_speed_wayid <- df_speed %>% 
  subset(way_id %in% df_hotedges_april16pt$way_id) %>% 
  group_by(way_id) %>% 
  summarise(avg_speed = mean((speed*18)/5), n = n() )

ggplot(avg_speed_wayid, aes(avg_speed)) +
  geom_histogram(binwidth = 5) + 
  scale_x_continuous(name = "Avg_speed(km/h)",  breaks = seq(0, 150, 10) )

Speed by week

Speed by day

Speed by hour

Number of session per hour

Ways_ids per half hour

Time serie per way_id

df_superhotedges_april16pt %>% 
  filter( way_id == list_osm_edge[2] ) %>% 
  plot_ly(x= ~time, y= ~(speed*18)/5, color = ~class) %>% 
  layout(title = paste("Time series of speed for way_id",list_osm_edge[2], sep = " " ),yaxis = list(title="Speed (km/h)"),
         xaxis = list(title="Date"))

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
minimal value for n is 3, returning requested palette with 3 different levels
No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
minimal value for n is 3, returning requested palette with 3 different levels